{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: tqdm in d:\\program\\anaconda\\envs\\ai\\lib\\site-packages (4.46.0)\n",
      "Requirement already satisfied: lightgbm in d:\\program\\anaconda\\envs\\ai\\lib\\site-packages (2.3.0)\n",
      "Requirement already satisfied: scipy in d:\\program\\anaconda\\envs\\ai\\lib\\site-packages (from lightgbm) (1.3.2)\n",
      "Requirement already satisfied: scikit-learn in d:\\program\\anaconda\\envs\\ai\\lib\\site-packages (from lightgbm) (0.22.1)\n",
      "Requirement already satisfied: numpy in d:\\program\\anaconda\\envs\\ai\\lib\\site-packages (from lightgbm) (1.17.4)\n",
      "Requirement already satisfied: joblib>=0.11 in d:\\program\\anaconda\\envs\\ai\\lib\\site-packages (from scikit-learn->lightgbm) (0.14.1)\n"
     ]
    }
   ],
   "source": [
    "! pip install tqdm\n",
    "! pip install lightgbm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "from sklearn.metrics import mean_squared_error,explained_variance_score\n",
    "from sklearn.model_selection import KFold\n",
    "import lightgbm as lgb\n",
    "import math\n",
    "import os\n",
    "from joblib import Parallel, delayed\n",
    "\n",
    "test_data_path = '../data/A_testData0531.csv'\n",
    "route_order_folder_path = '../data/route_order_data_0.6'\n",
    "port_path = '../data/port.csv'\n",
    "result_path = '../result_server_20200623_0.6.csv'\n",
    "\n",
    "# import moxing as mox\n",
    "# OBS_RES_PATH =  \"s3://ship-eta/result/result_server_20200622.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def format_data_type(data, mode='train'):\n",
    "    if mode=='test':\n",
    "        data['onboardDate'] = pd.to_datetime(data['onboardDate'], infer_datetime_format=True)\n",
    "        data['temp_timestamp'] = data['timestamp']\n",
    "        data['ETA'] = None\n",
    "        data['creatDate'] = None\n",
    "    data['loadingOrder'] = data['loadingOrder'].astype(str)\n",
    "    data['timestamp'] = pd.to_datetime(data['timestamp'], infer_datetime_format=True)\n",
    "    data['longitude'] = data['longitude'].astype(float)\n",
    "    data['latitude'] = data['latitude'].astype(float)\n",
    "    data['speed'] = data['speed'].astype(float)\n",
    "    data['TRANSPORT_TRACE'] = data['TRANSPORT_TRACE'].astype(str)\n",
    "    return data\n",
    "\n",
    "def get_test_data_info(path):\n",
    "    data = pd.read_csv(path) \n",
    "    test_trace_set = data['TRANSPORT_TRACE'].unique()\n",
    "    test_order_belong_to_trace = {}\n",
    "    for item in test_trace_set:\n",
    "        orders = data[data['TRANSPORT_TRACE'] == item]['loadingOrder'].unique()\n",
    "        test_order_belong_to_trace[item] = orders\n",
    "    return format_data_type(data, mode='test'), test_trace_set, test_order_belong_to_trace\n",
    "\n",
    "test_data_origin, test_trace_set, test_order_belong_to_trace = get_test_data_info(test_data_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_port_info():\n",
    "    port_data = {}\n",
    "    test_port_set = set()\n",
    "    for route in test_trace_set:\n",
    "        ports = route.split('-')\n",
    "        test_port_set = set.union(test_port_set, set(ports))\n",
    "    port_data_origin = pd.read_csv(port_path)\n",
    "    for item in port_data_origin.itertuples():\n",
    "        if getattr(item, 'TRANS_NODE_NAME') in test_port_set:\n",
    "            port_data[getattr(item, 'TRANS_NODE_NAME')] = {'LONGITUDE': getattr(item, 'LONGITUDE'),'LATITUDE': getattr(item, 'LATITUDE') }\n",
    "    return port_data\n",
    "port_data = get_port_info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_train_route_order_data(route):\n",
    "    route_order_data_path = os.path.join(route_order_folder_path, \"{}.csv\".format(route))\n",
    "    data = pd.read_csv(route_order_data_path, header=None, usecols = [0,1,2,3,4]\n",
    "           , names=['loadingOrder','timestamp','longitude','latitude','speed']\n",
    "           , dtype={'longitude': np.float32, 'latitude': np.float32, 'speed': np.float32})\n",
    "\n",
    "    if (data.shape[0] == 0):\n",
    "        print(\"error == \", route)\n",
    "    data['timestamp'] = pd.to_datetime(data['timestamp'], infer_datetime_format=True)\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# agg_dict = {'latitude':['mean', 'median'], 'longitude':['mean', 'median'], 'speed':['max', 'mean', 'median']}\n",
    "agg_dict = {'latitude':['max','min','median'], 'longitude':['max','min','median'], 'speed':['mean','median']}\n",
    "\n",
    "cols = ['{}_{}'.format(k1, k2) for k1 in agg_dict for k2 in agg_dict[k1]]\n",
    "eps = 0.001\n",
    "\n",
    "def handle_train_data(order_info_set,start_longitude,start_latitude,dest_longitude,dest_latitude):\n",
    "    order_info_set = order_info_set.reset_index(drop=True)\n",
    "    #       获取起航时间\n",
    "    start_time = order_info_set['timestamp'].min()\n",
    "    start_index = 0\n",
    "    for (index, info_item) in order_info_set.iterrows():\n",
    "        if abs(info_item['longitude']-start_longitude) < 1.5 and abs(info_item['latitude']-start_latitude) < 1.5 and info_item['speed'] > 0:\n",
    "            start_time = max(start_time, info_item['timestamp'])\n",
    "            start_index = index\n",
    "            break \n",
    "#       获取到达目的地时间，这里需要用 GPS 判断\n",
    "    end_time = order_info_set['timestamp'].max()\n",
    "    end_index = order_info_set.size-1\n",
    "    for (index, info_item) in order_info_set.iterrows():\n",
    "        if abs(info_item['longitude'] - dest_longitude) < 1.5 and abs(info_item['latitude'] - dest_latitude) < 1.5:\n",
    "            end_time = min(end_time, info_item['timestamp'])\n",
    "            end_index = index\n",
    "            break\n",
    "\n",
    "#         修正起点终点逆序\n",
    "    if (end_time < start_time):\n",
    "        start_time,end_time = end_time,start_time\n",
    "        start_index,end_index = end_index,start_index\n",
    "\n",
    "#         人工截取前 40% 的数据   \n",
    "    order_info_set = order_info_set[start_index:end_index+1]\n",
    "    cut_size = math.ceil(order_info_set.shape[0]*0.4)\n",
    "    order_info_set = order_info_set[0:cut_size]\n",
    "#         截取数据\n",
    "    if (order_info_set.shape[0] > 100):\n",
    "        index = np.linspace(0, order_info_set.shape[0]-1, num=100,dtype=int).tolist()\n",
    "        order_info_set = order_info_set.iloc[index]     \n",
    "        \n",
    "    print(order_info_set)\n",
    "        \n",
    "#         获取经纬度速度信息\n",
    "    feature_temp = order_info_set.groupby('loadingOrder').agg(agg_dict).reset_index()\n",
    "    feature_temp.columns = ['loadingOrder'] + cols\n",
    "#         算出航行用时\n",
    "    feature_temp['label'] = (end_time - start_time).total_seconds()\n",
    "    return feature_temp   \n",
    "\n",
    "def get_train_data(route_order_info, route):\n",
    "    ports = route.split(\"-\")\n",
    "    start_port = ports[0]\n",
    "    dest_port = ports[-1]\n",
    "    start_longitude = port_data[start_port]['LONGITUDE']\n",
    "    start_latitude = port_data[start_port]['LATITUDE']\n",
    "    dest_longitude = port_data[dest_port]['LONGITUDE']\n",
    "    dest_latitude = port_data[dest_port]['LATITUDE']\n",
    "    order_list = route_order_info['loadingOrder'].unique()\n",
    "    print(route_order_info)\n",
    "    print(order_list)\n",
    "    print(route, order_list.shape)\n",
    "    \n",
    "    data_grouped = route_order_info.groupby('loadingOrder')\n",
    "\n",
    "    train_data = Parallel(n_jobs=8)(delayed(handle_train_data)\n",
    "                                    (group,start_longitude,start_latitude,dest_longitude,dest_latitude)\n",
    "                                    for name, group in tqdm(data_grouped))\n",
    "    train_data = pd.concat(train_data)\n",
    "    \n",
    "    if (train_data.shape[0] < 10):\n",
    "        for i in range(5):\n",
    "            train_data = pd.concat([train_data,train_data])\n",
    "            \n",
    "    parameter_mean = train_data[cols].mean()\n",
    "    parameter_std = train_data[cols].std() + eps\n",
    "    train_data[cols] = (train_data[cols] - parameter_mean) / parameter_std\n",
    "    return parameter_mean,parameter_std,train_data.reset_index(drop=True)\n",
    "\n",
    "def get_test_data(order,parameter_mean,parameter_std):\n",
    "    order_info_set = test_data_origin[test_data_origin['loadingOrder'] == order].sort_values(by='timestamp')\n",
    "    feature = order_info_set.groupby('loadingOrder').agg(agg_dict).reset_index()\n",
    "    feature.columns = ['loadingOrder'] + cols\n",
    "    feature[cols] = (feature[cols] - parameter_mean) / parameter_std\n",
    "    return feature.reset_index(drop=True)\n",
    "def mse_score_eval(preds, valid):\n",
    "    labels = valid.get_label()\n",
    "    scores = mean_squared_error(y_true=labels, y_pred=preds)\n",
    "    return 'mse_score', scores, True\n",
    "def train_model(x, y, seed=981125, is_shuffle=True):\n",
    "    train_pred = np.zeros((x.shape[0], ))\n",
    "    n_splits = min(5, x.shape[0])\n",
    "    # Kfold\n",
    "    fold = KFold(n_splits=n_splits, shuffle=is_shuffle, random_state=seed)\n",
    "    kf_way = fold.split(x)\n",
    "    # params\n",
    "    params = {\n",
    "        'learning_rate': 0.01,\n",
    "        'boosting_type': 'gbdt',\n",
    "        'objective': 'regression',\n",
    "        'num_leaves': 36,\n",
    "        'feature_fraction': 0.6,\n",
    "        'bagging_fraction': 0.7,\n",
    "        'bagging_freq': 6,\n",
    "        'seed': 8,\n",
    "        'bagging_seed': 1,\n",
    "        'feature_fraction_seed': 7,\n",
    "        'min_data_in_leaf': 36,\n",
    "        'nthread': 8,\n",
    "        'verbose': 1,\n",
    "    }\n",
    "    # train\n",
    "    for n_fold, (train_idx, valid_idx) in enumerate(kf_way, start=1):\n",
    "        train_x, train_y = x.iloc[train_idx], y.iloc[train_idx]\n",
    "        valid_x, valid_y = x.iloc[valid_idx], y.iloc[valid_idx]\n",
    "        # 数据加载\n",
    "        n_train = lgb.Dataset(train_x, label=train_y)\n",
    "        n_valid = lgb.Dataset(valid_x, label=valid_y)\n",
    "        clf = lgb.train(\n",
    "            params=params,\n",
    "            train_set=n_train,\n",
    "            num_boost_round=3000,\n",
    "            valid_sets=[n_valid],\n",
    "            early_stopping_rounds=100,\n",
    "            verbose_eval=100,\n",
    "            feval=mse_score_eval\n",
    "        )\n",
    "        train_pred[valid_idx] = clf.predict(valid_x, num_iteration=clf.best_iteration)\n",
    "    return clf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/22 [00:00<?, ?it/s]"
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            loadingOrder                 timestamp  longitude   latitude  \\\n",
      "0         GF912387439043 2019-01-01 00:05:46+00:00 -74.893997 -25.123119   \n",
      "1         XR467412052881 2019-01-01 00:05:46+00:00 -74.893997 -25.123119   \n",
      "2         GF912387439043 2019-01-01 00:05:52+00:00 -74.893867 -25.123573   \n",
      "3         XR467412052881 2019-01-01 00:05:52+00:00 -74.893867 -25.123573   \n",
      "4         GF912387439043 2019-01-01 00:06:29+00:00 -74.892990 -25.126106   \n",
      "...                  ...                       ...        ...        ...   \n",
      "15805558  MY883104806169 2020-04-29 07:13:08+00:00 -77.204300 -12.018998   \n",
      "15805559  WT449349623447 2020-04-29 07:13:08+00:00 -77.204300 -12.018998   \n",
      "15805560  NY431247816698 2020-04-29 07:13:08+00:00 -77.204300 -12.018998   \n",
      "15805561  DT980833467912 2020-04-29 07:13:08+00:00 -77.204300 -12.018998   \n",
      "15805562  LF137444375241 2020-04-29 07:13:08+00:00 -77.204300 -12.018998   \n",
      "\n",
      "          speed  \n",
      "0          29.0  \n",
      "1          29.0  \n",
      "2          29.0  \n",
      "3          29.0  \n",
      "4          29.0  \n",
      "...         ...  \n",
      "15805558    0.0  \n",
      "15805559    0.0  \n",
      "15805560    0.0  \n",
      "15805561    0.0  \n",
      "15805562    0.0  \n",
      "\n",
      "[15805563 rows x 5 columns]\n",
      "['GF912387439043' 'XR467412052881' 'NF584265694710' ... 'TQ936012130249'\n",
      " 'YX161521652786' 'QY429965776699']\n",
      "CNYTN-MXZLO (1137,)\n"
     ]
    },
    {
     "name": "stderr",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 4.9309e+10\tvalid_0's mse_score: 4.9309e+10\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 8.45599e+10\tvalid_0's mse_score: 8.45599e+10\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 3.99453e+10\tvalid_0's mse_score: 3.99453e+10\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 8.06099e+10\tvalid_0's mse_score: 8.06099e+10\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 3.83399e+10\tvalid_0's mse_score: 3.83399e+10\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 7.78343e+10\tvalid_0's mse_score: 7.78343e+10\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 5.51098e+10\tvalid_0's mse_score: 5.51098e+10\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 1.01968e+11\tvalid_0's mse_score: 1.01968e+11\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 4.89161e+10\tvalid_0's mse_score: 4.89161e+10\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 8.98785e+10\tvalid_0's mse_score: 8.98785e+10\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "  5%|▍         | 1/22 [05:58<2:05:37, 358.93s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            loadingOrder                 timestamp   longitude   latitude  \\\n",
      "0         DC246820574688 2019-01-01 00:02:38+00:00  113.886665  22.410950   \n",
      "1         CQ961324913509 2019-01-01 00:03:40+00:00  103.388260   1.219455   \n",
      "2         CN906368143939 2019-01-01 00:03:40+00:00  103.388260   1.219455   \n",
      "3         PE175840129874 2019-01-01 00:03:55+00:00  121.685371  23.045778   \n",
      "4         NJ987920211042 2019-01-01 00:03:55+00:00  121.685371  23.045778   \n",
      "...                  ...                       ...         ...        ...   \n",
      "97512718  SL105104110835 2020-04-29 07:13:59+00:00  103.780632   1.242283   \n",
      "97512719  RY534921129803 2020-04-29 07:15:20+00:00    3.180860   6.315635   \n",
      "97512720  YH250004494091 2020-04-29 07:15:20+00:00    3.180860   6.315635   \n",
      "97512721  DM517244187157 2020-04-29 07:15:20+00:00    3.180860   6.315635   \n",
      "97512722  OM585194392203 2020-04-29 07:15:20+00:00    3.180860   6.315635   \n",
      "\n",
      "          speed  \n",
      "0          15.0  \n",
      "1          21.0  \n",
      "2          21.0  \n",
      "3          31.0  \n",
      "4          31.0  \n",
      "...         ...  \n",
      "97512718   13.0  \n",
      "97512719    0.0  \n",
      "97512720    0.0  \n",
      "97512721    0.0  \n",
      "97512722    0.0  \n",
      "\n",
      "[97512723 rows x 5 columns]\n",
      "['DC246820574688' 'CQ961324913509' 'CN906368143939' ... 'TF969909453770'\n",
      " 'BY107977824729' 'CD518251283030']\n",
      "CNSHK-MYTPP (10619,)\n"
     ]
    },
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     "output_type": "stream",
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     ]
    },
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     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 1.22828e+10\tvalid_0's mse_score: 1.22828e+10\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 1.9443e+10\tvalid_0's mse_score: 1.9443e+10\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 1.59877e+10\tvalid_0's mse_score: 1.59877e+10\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 2.22864e+10\tvalid_0's mse_score: 2.22864e+10\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 1.60538e+10\tvalid_0's mse_score: 1.60538e+10\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 2.27647e+10\tvalid_0's mse_score: 2.27647e+10\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 7.83645e+09\tvalid_0's mse_score: 7.83645e+09\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 1.42666e+10\tvalid_0's mse_score: 1.42666e+10\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 8.3459e+09\tvalid_0's mse_score: 8.3459e+09\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 1.30939e+10\tvalid_0's mse_score: 1.30939e+10\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "  9%|▉         | 2/22 [26:37<3:27:37, 622.86s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            loadingOrder                 timestamp   longitude   latitude  \\\n",
      "0         DC246820574688 2019-01-01 00:02:38+00:00  113.886665  22.410950   \n",
      "1         CQ961324913509 2019-01-01 00:03:40+00:00  103.388260   1.219455   \n",
      "2         CN906368143939 2019-01-01 00:03:40+00:00  103.388260   1.219455   \n",
      "3         PE175840129874 2019-01-01 00:03:55+00:00  121.685371  23.045778   \n",
      "4         NJ987920211042 2019-01-01 00:03:55+00:00  121.685371  23.045778   \n",
      "...                  ...                       ...         ...        ...   \n",
      "97549675  SL105104110835 2020-04-29 07:13:59+00:00  103.780632   1.242283   \n",
      "97549676  RY534921129803 2020-04-29 07:15:20+00:00    3.180860   6.315635   \n",
      "97549677  YH250004494091 2020-04-29 07:15:20+00:00    3.180860   6.315635   \n",
      "97549678  DM517244187157 2020-04-29 07:15:20+00:00    3.180860   6.315635   \n",
      "97549679  OM585194392203 2020-04-29 07:15:20+00:00    3.180860   6.315635   \n",
      "\n",
      "          speed  \n",
      "0          15.0  \n",
      "1          21.0  \n",
      "2          21.0  \n",
      "3          31.0  \n",
      "4          31.0  \n",
      "...         ...  \n",
      "97549675   13.0  \n",
      "97549676    0.0  \n",
      "97549677    0.0  \n",
      "97549678    0.0  \n",
      "97549679    0.0  \n",
      "\n",
      "[97549680 rows x 5 columns]\n",
      "['DC246820574688' 'CQ961324913509' 'CN906368143939' ... 'IJ983647444250'\n",
      " 'UH900407977127' 'AQ303922271436']\n",
      "CNSHK-SGSIN (10670,)\n"
     ]
    },
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 9.3585e+09\tvalid_0's mse_score: 9.3585e+09\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 1.58072e+10\tvalid_0's mse_score: 1.58072e+10\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 1.80158e+10\tvalid_0's mse_score: 1.80158e+10\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 2.48095e+10\tvalid_0's mse_score: 2.48095e+10\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 1.73712e+10\tvalid_0's mse_score: 1.73712e+10\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 2.53252e+10\tvalid_0's mse_score: 2.53252e+10\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 8.48669e+09\tvalid_0's mse_score: 8.48669e+09\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 1.41751e+10\tvalid_0's mse_score: 1.41751e+10\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 6.69732e+09\tvalid_0's mse_score: 6.69732e+09\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 1.15085e+10\tvalid_0's mse_score: 1.15085e+10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 14%|█▎        | 3/22 [48:15<4:21:21, 825.36s/it]\n",
      "  0%|          | 0/92 [00:00<?, ?it/s]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           loadingOrder                 timestamp  longitude   latitude  speed\n",
      "0        GF912387439043 2019-01-01 00:05:46+00:00 -74.893997 -25.123119   29.0\n",
      "1        XR467412052881 2019-01-01 00:05:46+00:00 -74.893997 -25.123119   29.0\n",
      "2        GF912387439043 2019-01-01 00:05:52+00:00 -74.893867 -25.123573   29.0\n",
      "3        XR467412052881 2019-01-01 00:05:52+00:00 -74.893867 -25.123573   29.0\n",
      "4        GF912387439043 2019-01-01 00:06:29+00:00 -74.892990 -25.126106   29.0\n",
      "...                 ...                       ...        ...        ...    ...\n",
      "1626670  TK459472155337 2020-04-23 09:57:39+00:00 -71.648689 -33.580799    7.0\n",
      "1626671  TK459472155337 2020-04-23 10:00:29+00:00 -71.645561 -33.581993    6.0\n",
      "1626672  TK459472155337 2020-04-23 10:22:58+00:00 -71.621460 -33.586761    2.0\n",
      "1626673  TK459472155337 2020-04-23 10:41:26+00:00 -71.621483 -33.589230    0.0\n",
      "1626674  TK459472155337 2020-04-23 11:26:27+00:00 -71.621483 -33.589214    0.0\n",
      "\n",
      "[1626675 rows x 5 columns]\n",
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      " 'AC118422359182' 'PL486322563493' 'TP518047611804' 'IF896260473107'\n",
      " 'YW638325217174' 'FJ139888238837' 'AP254924452907' 'EF941194071265'\n",
      " 'YG744864293851' 'OY102324684975' 'RE258666123467' 'PI577751632071'\n",
      " 'YD480640646352' 'BR827526507811' 'SK180257851043' 'KT387556157937'\n",
      " 'HU800177043679' 'TP619621456893' 'CM835257614974' 'YW391046018891'\n",
      " 'VJ203577792370' 'CO475888659304' 'DJ518662801757' 'YS361365535403'\n",
      " 'CT126357249039' 'AC458999327091' 'KY654516947756' 'SB635555121477'\n",
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      " 'XH377850217192' 'PU178837835960' 'DY127695529885' 'JG346490911383'\n",
      " 'DB249537838867' 'DN703453329569' 'JG473275827112' 'YQ600227678834'\n",
      " 'MF304389434347' 'EM651586750244' 'OE981574426101' 'ZM381066091094'\n",
      " 'ZU416619710839' 'EF766484434827' 'YP970675234519' 'EG236620147337'\n",
      " 'CY244086441662' 'IV706975896259' 'BS116418820831' 'LE824782286915'\n",
      " 'XU995869374983' 'KZ841230219240' 'CH115924710387' 'LG375281951253'\n",
      " 'GL877261561202' 'ND494956380462' 'WF211766643371' 'FE486474293552'\n",
      " 'EM639816929631' 'KI409210235789' 'VR702388725358' 'OE565790008242'\n",
      " 'CN458684374185' 'AS953078762857' 'XH178818697012' 'RM988806084309'\n",
      " 'EN419572789582' 'CZ393133473165' 'PC749122315679' 'GR858347801908'\n",
      " 'SE930086888279' 'RQ820703498393' 'OE797412378478' 'MS717426468727'\n",
      " 'XV271491364545' 'UI266363703371' 'SM156145157129' 'WZ424206998330'\n",
      " 'TQ468389391403' 'LF119404767816' 'NV563102487795' 'TK459472155337']\n",
      "CNSHK-CLVAP (92,)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "  1%|          | 1/92 [00:00<00:18,  5.03it/s]\u001b[A\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 7.76073e+12\tvalid_0's mse_score: 7.76073e+12\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 7.76073e+12\tvalid_0's mse_score: 7.76073e+12\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 7.97507e+12\tvalid_0's mse_score: 7.97507e+12\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 7.97507e+12\tvalid_0's mse_score: 7.97507e+12\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 1.10034e+13\tvalid_0's mse_score: 1.10034e+13\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 1.10034e+13\tvalid_0's mse_score: 1.10034e+13\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 8.10098e+12\tvalid_0's mse_score: 8.10098e+12\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 8.10098e+12\tvalid_0's mse_score: 8.10098e+12\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 6.80187e+12\tvalid_0's mse_score: 6.80187e+12\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 6.80187e+12\tvalid_0's mse_score: 6.80187e+12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 18%|█▊        | 4/22 [49:15<2:58:44, 595.78s/it]\n",
      "  0%|          | 0/84 [00:00<?, ?it/s]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           loadingOrder                 timestamp  longitude   latitude  speed\n",
      "0        BT552005629187 2019-01-11 09:01:21+00:00 -56.206692 -34.899326    0.0\n",
      "1        BT552005629187 2019-01-11 09:01:21+00:00 -56.206692 -34.899326    0.0\n",
      "2        BT552005629187 2019-01-11 09:01:21+00:00 -56.206692 -34.899326    0.0\n",
      "3        BT552005629187 2019-01-11 09:07:21+00:00 -56.206688 -34.899292    0.0\n",
      "4        BT552005629187 2019-01-11 09:07:21+00:00 -56.206688 -34.899292    0.0\n",
      "...                 ...                       ...        ...        ...    ...\n",
      "1759079  AQ889642404042 2020-04-13 00:16:39+00:00 -58.370102 -34.577309    1.0\n",
      "1759080  FE496950848326 2020-04-13 00:19:58+00:00 -58.371037 -34.577484    0.0\n",
      "1759081  BX633511112869 2020-04-13 00:19:58+00:00 -58.371037 -34.577484    0.0\n",
      "1759082  LV736885071140 2020-04-13 00:19:58+00:00 -58.371037 -34.577484    0.0\n",
      "1759083  AQ889642404042 2020-04-13 00:19:58+00:00 -58.371037 -34.577484    0.0\n",
      "\n",
      "[1759084 rows x 5 columns]\n",
      "['BT552005629187' 'PT475872398467' 'PF228128378069' 'UZ516517270135'\n",
      " 'RV958320503843' 'JD528257243377' 'NY586422266439' 'LR806792535384'\n",
      " 'TQ447153554373' 'YQ940913761119' 'ES655578127940' 'YK310273042413'\n",
      " 'ZL336113063705' 'SC422066701017' 'GR406218127239' 'YB685513321013'\n",
      " 'QJ192569605337' 'LN283917056780' 'ER854397217299' 'TJ420051452377'\n",
      " 'KY903429934099' 'WD590991462067' 'RT994195209870' 'KE607377663581'\n",
      " 'LT765371422326' 'VI831394352797' 'CY845545728457' 'EG650737027142'\n",
      " 'FP321701482747' 'ZS104404854005' 'WH953939043629' 'EY912203485701'\n",
      " 'WD937588962444' 'VA459719357006' 'PD911827258622' 'XG469643043883'\n",
      " 'YF350944846013' 'TP583376192578' 'JC352845239087' 'CC797411810448'\n",
      " 'OD268575858476' 'SG944414903180' 'KT598516162837' 'RV378000607557'\n",
      " 'KS807266607348' 'YI367241100459' 'ZK570930733133' 'TP950585895085'\n",
      " 'TB325302729664' 'BK127471045315' 'HJ987635444529' 'FM281999430308'\n",
      " 'LB470440204848' 'ZB984347982509' 'KI623249147456' 'BQ425517215925'\n",
      " 'HY761808914932' 'QT943331705158' 'SJ698044844956' 'UL182902731423'\n",
      " 'TD509955657911' 'GM322029814676' 'CG784662471298' 'DC607670091891'\n",
      " 'HM747423724669' 'AU510504184664' 'QL886897266354' 'SJ798664010208'\n",
      " 'EJ606247409716' 'NQ868861742288' 'YY314907990848' 'WG231339554110'\n",
      " 'GH713404067383' 'RF384622703546' 'DN111744049708' 'LC886045047547'\n",
      " 'OT275157150394' 'YV228490865596' 'FZ481109895587' 'MT677788323282'\n",
      " 'BX633511112869' 'LV736885071140' 'FE496950848326' 'AQ889642404042']\n",
      "CNYTN-ARENA (84,)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "  1%|          | 1/84 [00:00<00:12,  6.45it/s]\u001b[A\n",
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      "100%|██████████| 84/84 [00:27<00:00,  3.10it/s]\u001b[A\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 1.01038e+13\tvalid_0's mse_score: 1.01038e+13\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 1.01038e+13\tvalid_0's mse_score: 1.01038e+13\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 1.01044e+13\tvalid_0's mse_score: 1.01044e+13\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 1.01044e+13\tvalid_0's mse_score: 1.01044e+13\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 9.85941e+12\tvalid_0's mse_score: 9.85941e+12\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 9.85941e+12\tvalid_0's mse_score: 9.85941e+12\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 1.07932e+13\tvalid_0's mse_score: 1.07932e+13\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 1.07932e+13\tvalid_0's mse_score: 1.07932e+13\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 9.77325e+12\tvalid_0's mse_score: 9.77325e+12\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 9.77325e+12\tvalid_0's mse_score: 9.77325e+12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 23%|██▎       | 5/22 [50:14<2:03:12, 434.85s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            loadingOrder                 timestamp   longitude   latitude  \\\n",
      "0         SG611668696936 2019-01-06 01:12:22+00:00  114.283897  22.566483   \n",
      "1         TO189890245620 2019-01-06 01:12:22+00:00  114.283897  22.566483   \n",
      "2         XD749867714230 2019-01-06 01:27:22+00:00  114.283836  22.566418   \n",
      "3         SG611668696936 2019-01-06 01:27:22+00:00  114.283836  22.566418   \n",
      "4         TO189890245620 2019-01-06 01:27:22+00:00  114.283836  22.566418   \n",
      "...                  ...                       ...         ...        ...   \n",
      "25131839  ZS899945031477 2020-04-29 07:13:17+00:00    9.865060  53.543388   \n",
      "25131840  QJ661481590428 2020-04-29 07:13:17+00:00    9.865060  53.543388   \n",
      "25131841  AP899244026004 2020-04-29 07:13:17+00:00    9.865060  53.543388   \n",
      "25131842  YJ291162210770 2020-04-29 07:13:17+00:00    9.865060  53.543388   \n",
      "25131843  DW613071562755 2020-04-29 07:13:36+00:00  -17.345543  14.152653   \n",
      "\n",
      "          speed  \n",
      "0           0.0  \n",
      "1           0.0  \n",
      "2           0.0  \n",
      "3           0.0  \n",
      "4           0.0  \n",
      "...         ...  \n",
      "25131839   11.0  \n",
      "25131840   11.0  \n",
      "25131841   11.0  \n",
      "25131842   11.0  \n",
      "25131843   27.0  \n",
      "\n",
      "[25131844 rows x 5 columns]\n",
      "['SG611668696936' 'TO189890245620' 'XD749867714230' ... 'HQ676454890490'\n",
      " 'XA491854130434' 'WO981221351272']\n",
      "CNYTN-MATNG (1957,)\n"
     ]
    },
    {
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 2.51074e+10\tvalid_0's mse_score: 2.51074e+10\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 3.57817e+10\tvalid_0's mse_score: 3.57817e+10\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 1.8121e+10\tvalid_0's mse_score: 1.8121e+10\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 2.80255e+10\tvalid_0's mse_score: 2.80255e+10\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 2.82386e+10\tvalid_0's mse_score: 2.82386e+10\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 3.90213e+10\tvalid_0's mse_score: 3.90213e+10\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 3.20321e+10\tvalid_0's mse_score: 3.20321e+10\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 4.17975e+10\tvalid_0's mse_score: 4.17975e+10\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 2.64855e+10\tvalid_0's mse_score: 2.64855e+10\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 3.50252e+10\tvalid_0's mse_score: 3.50252e+10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 27%|██▋       | 6/22 [58:22<2:00:08, 450.53s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           loadingOrder                 timestamp  longitude   latitude  speed\n",
      "0        FC114934484860 2019-01-08 15:54:22+00:00  21.161060  55.645622    0.0\n",
      "1        IE161315769717 2019-01-08 15:54:22+00:00  21.161060  55.645622    0.0\n",
      "2        FC114934484860 2019-01-08 16:30:58+00:00  21.161062  55.645622    0.0\n",
      "3        IE161315769717 2019-01-08 16:30:58+00:00  21.161062  55.645622    0.0\n",
      "4        FC114934484860 2019-01-08 17:10:46+00:00  21.161057  55.645634    0.0\n",
      "...                 ...                       ...        ...        ...    ...\n",
      "3885159  DB302587484389 2020-04-29 07:09:56+00:00   9.867392  43.962254    0.0\n",
      "3885160  EH362211337304 2020-04-29 07:09:56+00:00   9.867392  43.962254    0.0\n",
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      "3885163  FG175564854757 2020-04-29 07:09:56+00:00   9.867392  43.962254    0.0\n",
      "\n",
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      " 'EI598415882529' 'GQ688306882548' 'EV768443454256']\n",
      "CNSHK-GRPIR (355,)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 3.73115e+10\tvalid_0's mse_score: 3.73115e+10\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 5.89297e+10\tvalid_0's mse_score: 5.89297e+10\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 7.69381e+10\tvalid_0's mse_score: 7.69381e+10\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 9.80948e+10\tvalid_0's mse_score: 9.80948e+10\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 9.66033e+10\tvalid_0's mse_score: 9.66033e+10\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 1.22937e+11\tvalid_0's mse_score: 1.22937e+11\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 6.36674e+10\tvalid_0's mse_score: 6.36674e+10\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 8.98356e+10\tvalid_0's mse_score: 8.98356e+10\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 9.23774e+10\tvalid_0's mse_score: 9.23774e+10\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 1.10471e+11\tvalid_0's mse_score: 1.10471e+11\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 32%|███▏      | 7/22 [59:33<1:24:13, 336.88s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           loadingOrder                 timestamp   longitude   latitude  \\\n",
      "0        NO583073595816 2019-01-07 06:27:45+00:00  113.885147  22.451033   \n",
      "1        NO583073595816 2019-01-07 06:57:46+00:00  113.885162  22.451033   \n",
      "2        NO583073595816 2019-01-07 07:30:47+00:00  113.885162  22.451038   \n",
      "3        NO583073595816 2019-01-07 07:47:24+00:00  113.884140  22.451799   \n",
      "4        NO583073595816 2019-01-07 07:50:51+00:00  113.883102  22.452707   \n",
      "...                 ...                       ...         ...        ...   \n",
      "2278327  KD946733856445 2020-04-29 07:08:39+00:00   66.991508  23.905989   \n",
      "2278328  VP788685556867 2020-04-29 07:08:39+00:00   66.991508  23.905989   \n",
      "2278329  SQ639896150143 2020-04-29 07:08:39+00:00   66.991508  23.905989   \n",
      "2278330  BW738935004512 2020-04-29 07:13:11+00:00   66.974098  24.825069   \n",
      "2278331  NN802184540952 2020-04-29 07:13:11+00:00   66.974098  24.825069   \n",
      "\n",
      "         speed  \n",
      "0          0.0  \n",
      "1          0.0  \n",
      "2          0.0  \n",
      "3          0.0  \n",
      "4          4.0  \n",
      "...        ...  \n",
      "2278327   22.0  \n",
      "2278328   22.0  \n",
      "2278329   22.0  \n",
      "2278330    0.0  \n",
      "2278331    0.0  \n",
      "\n",
      "[2278332 rows x 5 columns]\n",
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      " 'IG521165645732']\n",
      "CNSHK-PKQCT (429,)\n"
     ]
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    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 1.09867e+10\tvalid_0's mse_score: 1.09867e+10\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 1.83464e+10\tvalid_0's mse_score: 1.83464e+10\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 9.69777e+09\tvalid_0's mse_score: 9.69777e+09\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 1.71976e+10\tvalid_0's mse_score: 1.71976e+10\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 1.38418e+10\tvalid_0's mse_score: 1.38418e+10\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 2.38731e+10\tvalid_0's mse_score: 2.38731e+10\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 1.23195e+10\tvalid_0's mse_score: 1.23195e+10\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 2.17741e+10\tvalid_0's mse_score: 2.17741e+10\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 7.59948e+09\tvalid_0's mse_score: 7.59948e+09\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 1.43087e+10\tvalid_0's mse_score: 1.43087e+10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      " 36%|███▋      | 8/22 [1:00:32<59:06, 253.34s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "           loadingOrder                 timestamp   longitude   latitude  \\\n",
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      "...                 ...                       ...         ...        ...   \n",
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      "\n",
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      "COBUN-HKHKG (298,)\n"
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      "\n",
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     "text": [
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 1.44979e+10\tvalid_0's mse_score: 1.44979e+10\n",
      "Early stopping, best iteration is:\n",
      "[2]\tvalid_0's l2: 1.50748e+10\tvalid_0's mse_score: 1.50748e+10\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 3.66329e+10\tvalid_0's mse_score: 3.66329e+10\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 3.99148e+10\tvalid_0's mse_score: 3.99148e+10\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 5.38217e+09\tvalid_0's mse_score: 5.38217e+09\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 7.07926e+09\tvalid_0's mse_score: 7.07926e+09\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 1.407e+11\tvalid_0's mse_score: 1.407e+11\n",
      "Early stopping, best iteration is:\n",
      "[43]\tvalid_0's l2: 1.40336e+11\tvalid_0's mse_score: 1.40336e+11\n",
      "Training until validation scores don't improve for 100 rounds\n"
     ]
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     "output_type": "stream",
     "text": [
      " 41%|████      | 9/22 [1:02:24<45:42, 210.98s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[100]\tvalid_0's l2: 2.81114e+10\tvalid_0's mse_score: 2.81114e+10\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 3.03055e+10\tvalid_0's mse_score: 3.03055e+10\n",
      "           loadingOrder                 timestamp   longitude   latitude  \\\n",
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      "4        WV121595634696 2019-01-01 01:47:30+00:00  114.283699  22.577808   \n",
      "...                 ...                       ...         ...        ...   \n",
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      "\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 1.42268e+11\tvalid_0's mse_score: 1.42268e+11\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 2.28312e+11\tvalid_0's mse_score: 2.28312e+11\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 1.10638e+11\tvalid_0's mse_score: 1.10638e+11\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 1.95021e+11\tvalid_0's mse_score: 1.95021e+11\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 1.46804e+11\tvalid_0's mse_score: 1.46804e+11\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 2.36038e+11\tvalid_0's mse_score: 2.36038e+11\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 2.23033e+11\tvalid_0's mse_score: 2.23033e+11\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 3.56943e+11\tvalid_0's mse_score: 3.56943e+11\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 1.88652e+11\tvalid_0's mse_score: 1.88652e+11\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 2.92047e+11\tvalid_0's mse_score: 2.92047e+11\n"
     ]
    },
    {
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     "text": [
      " 45%|████▌     | 10/22 [1:05:42<41:25, 207.09s/it]"
     ]
    },
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     "text": [
      "           loadingOrder                 timestamp   longitude   latitude  \\\n",
      "0        IY966180801664 2019-01-05 20:41:43+00:00  113.877365  22.462536   \n",
      "1        HS228419985503 2019-01-05 20:41:43+00:00  113.877365  22.462536   \n",
      "2        LK474000157357 2019-01-05 20:53:45+00:00  113.877380  22.462536   \n",
      "3        IY966180801664 2019-01-05 20:53:45+00:00  113.877380  22.462536   \n",
      "4        HS228419985503 2019-01-05 20:53:45+00:00  113.877380  22.462536   \n",
      "...                 ...                       ...         ...        ...   \n",
      "5905745  ZN784215088902 2020-04-29 07:00:23+00:00   13.733533  45.553032   \n",
      "5905746  BE119660887182 2020-04-29 07:00:23+00:00   13.733533  45.553032   \n",
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      "5905748  ZN784215088902 2020-04-29 07:06:20+00:00   13.733558  45.553020   \n",
      "5905749  BE119660887182 2020-04-29 07:06:20+00:00   13.733558  45.553020   \n",
      "\n",
      "         speed  \n",
      "0          0.0  \n",
      "1          0.0  \n",
      "2          0.0  \n",
      "3          0.0  \n",
      "4          0.0  \n",
      "...        ...  \n",
      "5905745    0.0  \n",
      "5905746    0.0  \n",
      "5905747    0.0  \n",
      "5905748    0.0  \n",
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      "\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 6.64466e+10\tvalid_0's mse_score: 6.64466e+10\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 2.30028e+11\tvalid_0's mse_score: 2.30028e+11\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 3.87457e+10\tvalid_0's mse_score: 3.87457e+10\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 1.33117e+11\tvalid_0's mse_score: 1.33117e+11\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 3.75998e+10\tvalid_0's mse_score: 3.75998e+10\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 1.63674e+11\tvalid_0's mse_score: 1.63674e+11\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 4.38388e+10\tvalid_0's mse_score: 4.38388e+10\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 1.5101e+11\tvalid_0's mse_score: 1.5101e+11\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 5.99356e+10\tvalid_0's mse_score: 5.99356e+10\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 1.72493e+11\tvalid_0's mse_score: 1.72493e+11\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 50%|█████     | 11/22 [1:07:56<33:57, 185.21s/it]\n",
      "  0%|          | 0/178 [00:00<?, ?it/s]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           loadingOrder                 timestamp   longitude   latitude  \\\n",
      "0        BF148288797241 2019-01-01 00:08:43+00:00  114.260918  22.571417   \n",
      "1        BF148288797241 2019-01-01 00:23:42+00:00  114.260864  22.571632   \n",
      "2        BF148288797241 2019-01-01 00:35:44+00:00  114.260963  22.571583   \n",
      "3        BF148288797241 2019-01-01 00:50:44+00:00  114.260902  22.571568   \n",
      "4        BF148288797241 2019-01-01 01:05:42+00:00  114.260933  22.571583   \n",
      "...                 ...                       ...         ...        ...   \n",
      "1334924  FY390777951049 2020-04-21 06:23:27+00:00 -123.153450  49.016968   \n",
      "1334925  HM300136207511 2020-04-21 06:26:39+00:00 -123.153519  49.016884   \n",
      "1334926  JR403220883315 2020-04-21 06:26:39+00:00 -123.153519  49.016884   \n",
      "1334927  CA481222856960 2020-04-21 06:26:39+00:00 -123.153519  49.016884   \n",
      "1334928  GV844377125763 2020-04-21 06:26:39+00:00 -123.153519  49.016884   \n",
      "\n",
      "         speed  \n",
      "0          0.0  \n",
      "1          0.0  \n",
      "2          0.0  \n",
      "3          0.0  \n",
      "4          0.0  \n",
      "...        ...  \n",
      "1334924    0.0  \n",
      "1334925    0.0  \n",
      "1334926    0.0  \n",
      "1334927    0.0  \n",
      "1334928    0.0  \n",
      "\n",
      "[1334929 rows x 5 columns]\n",
      "['BF148288797241' 'WR282198399651' 'DV424581834926' 'CY283583201434'\n",
      " 'YP170311961113' 'FX645284700100' 'FL270960159301' 'OX932950228093'\n",
      " 'XQ478327795548' 'WU347922511622' 'HS184270645869' 'YR966003466074'\n",
      " 'CQ281727211468' 'JQ341169684843' 'OC216552557876' 'GW368536410316'\n",
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      " 'BY566678152177' 'RH373746282801' 'XH653261354191' 'VZ339267427847'\n",
      " 'RK926055861798' 'CI848324865850' 'PX197825981941' 'DB737077387420'\n",
      " 'CT178890373391' 'GY690399120791' 'BM110762898592' 'WS669489990733'\n",
      " 'KQ354741544522' 'VQ756191807924' 'KD269357558938' 'FX510757491085'\n",
      " 'EQ410548352354' 'YY760734164887' 'IN779901355264' 'RN121625218050'\n",
      " 'VP387482673121' 'WW109123707598' 'EO618539140426' 'TC128369730234'\n",
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      " 'ZH603370675025' 'LF958909824071' 'QK574467206682' 'PX186353775191'\n",
      " 'JW327044120720' 'OW204444963742' 'TJ905855236835' 'JR178965711690'\n",
      " 'DF944373649251' 'LO507023184521' 'CY950138340007' 'QY579695317266'\n",
      " 'JY931280261142' 'YT550321938402' 'SY552317858038' 'SC357132000943'\n",
      " 'DV777385255574' 'GT557108973361' 'JK400181957481' 'OA795355733923'\n",
      " 'FM588961831826' 'EJ385308487226' 'WK919167548482' 'ZB861358603487'\n",
      " 'FW596405502640' 'ZD215105078416' 'VZ733512424807' 'RO151243100081'\n",
      " 'UL177886624581' 'OV197110126849' 'HM289584524955' 'TT880586479379'\n",
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      " 'QD484024195307' 'CF954531208696' 'UX683759881462' 'JN277046453827'\n",
      " 'WQ194935173783' 'LV642858853374' 'FN101264833709' 'EW251726531924'\n",
      " 'IW356622878635' 'XO554198990070' 'FU295218382247' 'VV188942054616'\n",
      " 'DL334745403119' 'BK311294032016' 'JM275693795595' 'UL408122477238'\n",
      " 'CW676627227818' 'SH888665528637' 'BQ990980681794' 'QJ167327550153'\n",
      " 'MR312272963590' 'NE208205307749' 'WM842005934074' 'NC235811812220'\n",
      " 'IU531335642699' 'SF485398704160' 'XS131192454800' 'YH143014069090'\n",
      " 'TB309363170312' 'HX557707825586' 'NA217144017691' 'PI228848539773'\n",
      " 'DV441829179756' 'PC313988278631' 'MC612987633559' 'ML208270379004'\n",
      " 'ZF166170112101' 'YH647415839675' 'BS857955595618' 'LR566943805621'\n",
      " 'TH701220953293' 'PO950257540721' 'BJ563425125106' 'FS376603829333'\n",
      " 'PD807594095841' 'NM400348664665' 'RV464719323004' 'UI847215344394'\n",
      " 'YQ139179844180' 'IU278410718477' 'PB202489293740' 'AC973431878181'\n",
      " 'YX344424007354' 'JL600042192411' 'YY850490269067' 'TB736698913511'\n",
      " 'YC249312417045' 'EA810961534626' 'YE788562824892' 'LF233168696125'\n",
      " 'AM630773305515' 'HO326714317285' 'EV807233447368' 'AL455172449176'\n",
      " 'JM908654622435' 'MF137152776052' 'FU702605888686' 'QO562055104388'\n",
      " 'FA213351856417' 'HB361027599463' 'HE453124147113' 'JR403220883315'\n",
      " 'HM300136207511' 'GV844377125763' 'CA481222856960' 'BI451930435624'\n",
      " 'XQ991420670733' 'FY390777951049']\n",
      "CNYTN-CAVAN (178,)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 1.13561e+10\tvalid_0's mse_score: 1.13561e+10\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 1.58852e+10\tvalid_0's mse_score: 1.58852e+10\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 1.909e+10\tvalid_0's mse_score: 1.909e+10\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 2.37463e+10\tvalid_0's mse_score: 2.37463e+10\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 1.39094e+10\tvalid_0's mse_score: 1.39094e+10\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 1.82268e+10\tvalid_0's mse_score: 1.82268e+10\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 2.0646e+10\tvalid_0's mse_score: 2.0646e+10\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 2.60693e+10\tvalid_0's mse_score: 2.60693e+10\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 1.44499e+10\tvalid_0's mse_score: 1.44499e+10\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 1.78893e+10\tvalid_0's mse_score: 1.78893e+10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 55%|█████▍    | 12/22 [1:08:29<23:16, 139.64s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            loadingOrder                 timestamp   longitude   latitude  \\\n",
      "0         SG611668696936 2019-01-06 01:12:22+00:00  114.283897  22.566483   \n",
      "1         TO189890245620 2019-01-06 01:12:22+00:00  114.283897  22.566483   \n",
      "2         XD749867714230 2019-01-06 01:27:22+00:00  114.283836  22.566418   \n",
      "3         SG611668696936 2019-01-06 01:27:22+00:00  114.283836  22.566418   \n",
      "4         TO189890245620 2019-01-06 01:27:22+00:00  114.283836  22.566418   \n",
      "...                  ...                       ...         ...        ...   \n",
      "25139590  ZS899945031477 2020-04-29 07:13:17+00:00    9.865060  53.543388   \n",
      "25139591  QJ661481590428 2020-04-29 07:13:17+00:00    9.865060  53.543388   \n",
      "25139592  AP899244026004 2020-04-29 07:13:17+00:00    9.865060  53.543388   \n",
      "25139593  YJ291162210770 2020-04-29 07:13:17+00:00    9.865060  53.543388   \n",
      "25139594  DW613071562755 2020-04-29 07:13:36+00:00  -17.345543  14.152653   \n",
      "\n",
      "          speed  \n",
      "0           0.0  \n",
      "1           0.0  \n",
      "2           0.0  \n",
      "3           0.0  \n",
      "4           0.0  \n",
      "...         ...  \n",
      "25139590   11.0  \n",
      "25139591   11.0  \n",
      "25139592   11.0  \n",
      "25139593   11.0  \n",
      "25139594   27.0  \n",
      "\n",
      "[25139595 rows x 5 columns]\n",
      "['SG611668696936' 'TO189890245620' 'XD749867714230' ... 'HQ676454890490'\n",
      " 'XA491854130434' 'WO981221351272']\n",
      "CNSHK-ESALG (1958,)\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 2.56281e+10\tvalid_0's mse_score: 2.56281e+10\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 3.71064e+10\tvalid_0's mse_score: 3.71064e+10\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 1.96943e+10\tvalid_0's mse_score: 1.96943e+10\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 2.95396e+10\tvalid_0's mse_score: 2.95396e+10\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 2.82389e+10\tvalid_0's mse_score: 2.82389e+10\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 3.89939e+10\tvalid_0's mse_score: 3.89939e+10\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 3.01335e+10\tvalid_0's mse_score: 3.01335e+10\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 3.99749e+10\tvalid_0's mse_score: 3.99749e+10\n",
      "Training until validation scores don't improve for 100 rounds\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 59%|█████▉    | 13/22 [1:16:42<36:50, 245.62s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[100]\tvalid_0's l2: 2.59752e+10\tvalid_0's mse_score: 2.59752e+10\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 3.41025e+10\tvalid_0's mse_score: 3.41025e+10\n",
      "            loadingOrder                 timestamp  longitude   latitude  \\\n",
      "0         SI319357417925 2019-01-01 00:12:21+00:00   2.218000  40.031731   \n",
      "1         SI319357417925 2019-01-01 00:12:21+00:00   2.218000  40.031731   \n",
      "2         SI319357417925 2019-01-01 00:12:21+00:00   2.218000  40.031731   \n",
      "3         SI319357417925 2019-01-01 01:27:16+00:00   2.547158  40.239861   \n",
      "4         SI319357417925 2019-01-01 01:27:16+00:00   2.547158  40.239861   \n",
      "...                  ...                       ...        ...        ...   \n",
      "28202223  ZS899945031477 2020-04-29 07:13:17+00:00   9.865060  53.543388   \n",
      "28202224  QJ661481590428 2020-04-29 07:13:17+00:00   9.865060  53.543388   \n",
      "28202225  AP899244026004 2020-04-29 07:13:17+00:00   9.865060  53.543388   \n",
      "28202226  YJ291162210770 2020-04-29 07:13:17+00:00   9.865060  53.543388   \n",
      "28202227  DW613071562755 2020-04-29 07:13:36+00:00 -17.345543  14.152653   \n",
      "\n",
      "          speed  \n",
      "0          29.0  \n",
      "1          29.0  \n",
      "2          29.0  \n",
      "3          29.0  \n",
      "4          29.0  \n",
      "...         ...  \n",
      "28202223   11.0  \n",
      "28202224   11.0  \n",
      "28202225   11.0  \n",
      "28202226   11.0  \n",
      "28202227   27.0  \n",
      "\n",
      "[28202228 rows x 5 columns]\n",
      "['SI319357417925' 'KI818545483287' 'SG611668696936' ... 'HQ676454890490'\n",
      " 'XA491854130434' 'WO981221351272']\n",
      "CNYTN-MTMLA (2323,)\n"
     ]
    },
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     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 2.91519e+10\tvalid_0's mse_score: 2.91519e+10\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 4.45267e+10\tvalid_0's mse_score: 4.45267e+10\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 3.77086e+10\tvalid_0's mse_score: 3.77086e+10\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 5.3732e+10\tvalid_0's mse_score: 5.3732e+10\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 3.27869e+10\tvalid_0's mse_score: 3.27869e+10\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 5.10704e+10\tvalid_0's mse_score: 5.10704e+10\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 2.90871e+10\tvalid_0's mse_score: 2.90871e+10\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 4.19348e+10\tvalid_0's mse_score: 4.19348e+10\n",
      "Training until validation scores don't improve for 100 rounds\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      " 64%|██████▎   | 14/22 [1:24:42<42:05, 315.74s/it]"
     ]
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     "text": [
      "[100]\tvalid_0's l2: 2.17424e+10\tvalid_0's mse_score: 2.17424e+10\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 3.66448e+10\tvalid_0's mse_score: 3.66448e+10\n",
      "           loadingOrder                 timestamp   longitude   latitude  \\\n",
      "0        YG709139525549 2019-01-07 17:51:50+00:00  113.879387  22.458578   \n",
      "1        YG709139525549 2019-01-07 17:51:50+00:00  113.879387  22.458578   \n",
      "2        YG709139525549 2019-01-07 18:06:51+00:00  113.879440  22.458595   \n",
      "3        YG709139525549 2019-01-07 18:06:51+00:00  113.879440  22.458595   \n",
      "4        YG709139525549 2019-01-07 18:21:53+00:00  113.879433  22.458637   \n",
      "...                 ...                       ...         ...        ...   \n",
      "4370757  MB408119192624 2020-04-29 06:57:03+00:00    0.931085   5.488877   \n",
      "4370758  MB408119192624 2020-04-29 07:00:52+00:00    0.948743   5.488697   \n",
      "4370759  MB408119192624 2020-04-29 07:05:15+00:00    0.969037   5.489090   \n",
      "4370760  MB408119192624 2020-04-29 07:09:46+00:00    0.989855   5.490795   \n",
      "4370761  MB408119192624 2020-04-29 07:12:46+00:00    1.003600   5.492947   \n",
      "\n",
      "         speed  \n",
      "0          0.0  \n",
      "1          0.0  \n",
      "2          0.0  \n",
      "3          0.0  \n",
      "4          0.0  \n",
      "...        ...  \n",
      "4370757   30.0  \n",
      "4370758   30.0  \n",
      "4370759   30.0  \n",
      "4370760   30.0  \n",
      "4370761   30.0  \n",
      "\n",
      "[4370762 rows x 5 columns]\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 3.90276e+10\tvalid_0's mse_score: 3.90276e+10\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 4.67256e+10\tvalid_0's mse_score: 4.67256e+10\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 2.36601e+10\tvalid_0's mse_score: 2.36601e+10\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 3.21253e+10\tvalid_0's mse_score: 3.21253e+10\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 1.35276e+10\tvalid_0's mse_score: 1.35276e+10\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 1.90344e+10\tvalid_0's mse_score: 1.90344e+10\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 8.12938e+09\tvalid_0's mse_score: 8.12938e+09\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 1.54156e+10\tvalid_0's mse_score: 1.54156e+10\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 1.3101e+10\tvalid_0's mse_score: 1.3101e+10\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 1.92535e+10\tvalid_0's mse_score: 1.92535e+10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 68%|██████▊   | 15/22 [1:26:14<29:01, 248.74s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           loadingOrder                 timestamp  longitude   latitude  speed\n",
      "0        NZ924528727091 2019-01-10 13:30:40+00:00  14.534883  35.821178    0.0\n",
      "1        NZ924528727091 2019-01-10 13:30:40+00:00  14.534883  35.821178    0.0\n",
      "2        NZ924528727091 2019-01-10 13:30:40+00:00  14.534883  35.821178    0.0\n",
      "3        NZ924528727091 2019-01-10 13:51:43+00:00  14.534892  35.821152    0.0\n",
      "4        NZ924528727091 2019-01-10 13:51:43+00:00  14.534892  35.821152    0.0\n",
      "...                 ...                       ...        ...        ...    ...\n",
      "2422444  IF372975761896 2020-04-29 07:03:18+00:00  14.540467  35.817242    0.0\n",
      "2422445  PX773659568615 2020-04-29 07:05:44+00:00   2.149630  41.309601    0.0\n",
      "2422446  UV919946119625 2020-04-29 07:10:39+00:00  14.540460  35.817253    0.0\n",
      "2422447  IF372975761896 2020-04-29 07:10:39+00:00  14.540460  35.817253    0.0\n",
      "2422448  PX773659568615 2020-04-29 07:11:48+00:00   2.149667  41.309666    0.0\n",
      "\n",
      "[2422449 rows x 5 columns]\n",
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      " 'EV525234894498' 'TY902950530758' 'NS541832044493']\n",
      "CNSHK-LBBEY (247,)\n"
     ]
    },
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 2.31618e+11\tvalid_0's mse_score: 2.31618e+11\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 2.62205e+11\tvalid_0's mse_score: 2.62205e+11\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 6.69998e+11\tvalid_0's mse_score: 6.69998e+11\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 7.15284e+11\tvalid_0's mse_score: 7.15284e+11\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 2.226e+11\tvalid_0's mse_score: 2.226e+11\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 2.46787e+11\tvalid_0's mse_score: 2.46787e+11\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 2.9561e+10\tvalid_0's mse_score: 2.9561e+10\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 3.85634e+10\tvalid_0's mse_score: 3.85634e+10\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 2.25098e+10\tvalid_0's mse_score: 2.25098e+10\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 2.48148e+10\tvalid_0's mse_score: 2.48148e+10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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      " 73%|███████▎  | 16/22 [1:27:00<18:46, 187.80s/it]\n",
      "  0%|          | 0/35 [00:00<?, ?it/s]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          loadingOrder                 timestamp   longitude   latitude  speed\n",
      "0       JR700587465924 2019-03-05 03:09:03+00:00  113.885597  22.448282    0.0\n",
      "1       JR700587465924 2019-03-05 03:09:03+00:00  113.885597  22.448282    0.0\n",
      "2       JR700587465924 2019-03-05 03:09:31+00:00  113.885582  22.448433    0.0\n",
      "3       JR700587465924 2019-03-05 03:09:31+00:00  113.885582  22.448433    0.0\n",
      "4       JR700587465924 2019-03-05 03:21:33+00:00  113.885597  22.448334    0.0\n",
      "...                ...                       ...         ...        ...    ...\n",
      "253799  VC489290307158 2020-04-29 06:15:20+00:00   65.092247 -13.726337   31.0\n",
      "253800  RB663275149834 2020-04-29 06:25:49+00:00   65.049637 -13.752523   31.0\n",
      "253801  VC489290307158 2020-04-29 06:25:49+00:00   65.049637 -13.752523   31.0\n",
      "253802  RB663275149834 2020-04-29 06:48:25+00:00   64.958511 -13.810877   31.0\n",
      "253803  VC489290307158 2020-04-29 06:48:25+00:00   64.958511 -13.810877   31.0\n",
      "\n",
      "[253804 rows x 5 columns]\n",
      "['JR700587465924' 'JK498976943408' 'HR968480428969' 'DJ637306579292'\n",
      " 'DJ744742965177' 'SS658297307641' 'CG748498665309' 'ZQ697559280838'\n",
      " 'HD757078123178' 'IX315214416556' 'PQ737266604633' 'KX166235769203'\n",
      " 'YC422095424611' 'GF348257108268' 'KR865395898158' 'XZ421384928721'\n",
      " 'YL576058909553' 'NM623980114110' 'ED215538245468' 'NZ488576270652'\n",
      " 'IE781150586270' 'HV334748455699' 'LH252856548215' 'HF198112679687'\n",
      " 'SC350978301198' 'SE247856078419' 'KB118536047229' 'HJ183864481856'\n",
      " 'NO938457832575' 'KM272191590457' 'BF170444883190' 'SE791570278913'\n",
      " 'VP567929626357' 'RB663275149834' 'VC489290307158']\n",
      "CNSHA-SGSIN (35,)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 46%|████▌     | 16/35 [00:00<00:00, 66.07it/s]\u001b[A\n",
      " 69%|██████▊   | 24/35 [00:01<00:00, 15.94it/s]\u001b[A\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 4.93178e+11\tvalid_0's mse_score: 4.93178e+11\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 4.93178e+11\tvalid_0's mse_score: 4.93178e+11\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 1.79295e+11\tvalid_0's mse_score: 1.79295e+11\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 1.79295e+11\tvalid_0's mse_score: 1.79295e+11\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 4.15175e+11\tvalid_0's mse_score: 4.15175e+11\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 4.15175e+11\tvalid_0's mse_score: 4.15175e+11\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 4.41303e+11\tvalid_0's mse_score: 4.41303e+11\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 4.41303e+11\tvalid_0's mse_score: 4.41303e+11\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 5.0168e+11\tvalid_0's mse_score: 5.0168e+11\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 5.0168e+11\tvalid_0's mse_score: 5.0168e+11\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 77%|███████▋  | 17/22 [1:27:06<11:07, 133.43s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            loadingOrder                 timestamp   longitude   latitude  \\\n",
      "0         SG611668696936 2019-01-06 01:12:22+00:00  114.283897  22.566483   \n",
      "1         TO189890245620 2019-01-06 01:12:22+00:00  114.283897  22.566483   \n",
      "2         XD749867714230 2019-01-06 01:27:22+00:00  114.283836  22.566418   \n",
      "3         SG611668696936 2019-01-06 01:27:22+00:00  114.283836  22.566418   \n",
      "4         TO189890245620 2019-01-06 01:27:22+00:00  114.283836  22.566418   \n",
      "...                  ...                       ...         ...        ...   \n",
      "13161699  LJ969838486912 2020-04-29 07:13:17+00:00    9.865060  53.543388   \n",
      "13161700  ZS899945031477 2020-04-29 07:13:17+00:00    9.865060  53.543388   \n",
      "13161701  QJ661481590428 2020-04-29 07:13:17+00:00    9.865060  53.543388   \n",
      "13161702  AP899244026004 2020-04-29 07:13:17+00:00    9.865060  53.543388   \n",
      "13161703  YJ291162210770 2020-04-29 07:13:17+00:00    9.865060  53.543388   \n",
      "\n",
      "          speed  \n",
      "0           0.0  \n",
      "1           0.0  \n",
      "2           0.0  \n",
      "3           0.0  \n",
      "4           0.0  \n",
      "...         ...  \n",
      "13161699   11.0  \n",
      "13161700   11.0  \n",
      "13161701   11.0  \n",
      "13161702   11.0  \n",
      "13161703   11.0  \n",
      "\n",
      "[13161704 rows x 5 columns]\n",
      "['SG611668696936' 'TO189890245620' 'XD749867714230' ... 'JQ199881371532'\n",
      " 'US714232505488' 'GR998789499038']\n",
      "CNYTN-RTM (1244,)\n"
     ]
    },
    {
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     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 4.62943e+10\tvalid_0's mse_score: 4.62943e+10\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 6.14026e+10\tvalid_0's mse_score: 6.14026e+10\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 1.42337e+10\tvalid_0's mse_score: 1.42337e+10\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 2.54871e+10\tvalid_0's mse_score: 2.54871e+10\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 8.47293e+10\tvalid_0's mse_score: 8.47293e+10\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 1.05172e+11\tvalid_0's mse_score: 1.05172e+11\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 2.03421e+10\tvalid_0's mse_score: 2.03421e+10\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 3.05243e+10\tvalid_0's mse_score: 3.05243e+10\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 1.54146e+10\tvalid_0's mse_score: 1.54146e+10\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 2.63463e+10\tvalid_0's mse_score: 2.63463e+10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 82%|████████▏ | 18/22 [1:32:14<12:22, 185.66s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "            loadingOrder                 timestamp  longitude   latitude  \\\n",
      "0         GF912387439043 2019-01-01 00:05:46+00:00 -74.893997 -25.123119   \n",
      "1         XR467412052881 2019-01-01 00:05:46+00:00 -74.893997 -25.123119   \n",
      "2         GF912387439043 2019-01-01 00:05:52+00:00 -74.893867 -25.123573   \n",
      "3         XR467412052881 2019-01-01 00:05:52+00:00 -74.893867 -25.123573   \n",
      "4         GF912387439043 2019-01-01 00:06:29+00:00 -74.892990 -25.126106   \n",
      "...                  ...                       ...        ...        ...   \n",
      "15805558  MY883104806169 2020-04-29 07:13:08+00:00 -77.204300 -12.018998   \n",
      "15805559  WT449349623447 2020-04-29 07:13:08+00:00 -77.204300 -12.018998   \n",
      "15805560  NY431247816698 2020-04-29 07:13:08+00:00 -77.204300 -12.018998   \n",
      "15805561  DT980833467912 2020-04-29 07:13:08+00:00 -77.204300 -12.018998   \n",
      "15805562  LF137444375241 2020-04-29 07:13:08+00:00 -77.204300 -12.018998   \n",
      "\n",
      "          speed  \n",
      "0          29.0  \n",
      "1          29.0  \n",
      "2          29.0  \n",
      "3          29.0  \n",
      "4          29.0  \n",
      "...         ...  \n",
      "15805558    0.0  \n",
      "15805559    0.0  \n",
      "15805560    0.0  \n",
      "15805561    0.0  \n",
      "15805562    0.0  \n",
      "\n",
      "[15805563 rows x 5 columns]\n",
      "['GF912387439043' 'XR467412052881' 'NF584265694710' ... 'TQ936012130249'\n",
      " 'YX161521652786' 'QY429965776699']\n",
      "CNHKG-MXZLO (1137,)\n"
     ]
    },
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     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 4.92129e+10\tvalid_0's mse_score: 4.92129e+10\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 8.46598e+10\tvalid_0's mse_score: 8.46598e+10\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 3.99347e+10\tvalid_0's mse_score: 3.99347e+10\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 8.06697e+10\tvalid_0's mse_score: 8.06697e+10\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 3.82042e+10\tvalid_0's mse_score: 3.82042e+10\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 7.79432e+10\tvalid_0's mse_score: 7.79432e+10\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 5.5114e+10\tvalid_0's mse_score: 5.5114e+10\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 1.02139e+11\tvalid_0's mse_score: 1.02139e+11\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 4.88057e+10\tvalid_0's mse_score: 4.88057e+10\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 8.99155e+10\tvalid_0's mse_score: 8.99155e+10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 86%|████████▋ | 19/22 [1:38:12<11:52, 237.52s/it]\n",
      "  0%|          | 0/82 [00:00<?, ?it/s]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           loadingOrder                 timestamp  longitude   latitude  speed\n",
      "0        SI319357417925 2019-01-01 00:12:21+00:00   2.218000  40.031731   29.0\n",
      "1        SI319357417925 2019-01-01 00:12:21+00:00   2.218000  40.031731   29.0\n",
      "2        SI319357417925 2019-01-01 00:12:21+00:00   2.218000  40.031731   29.0\n",
      "3        SI319357417925 2019-01-01 01:27:16+00:00   2.547158  40.239861   29.0\n",
      "4        SI319357417925 2019-01-01 01:27:16+00:00   2.547158  40.239861   29.0\n",
      "...                 ...                       ...        ...        ...    ...\n",
      "1126508  PX773659568615 2020-04-29 06:53:43+00:00   2.149622  41.309601    0.0\n",
      "1126509  PX773659568615 2020-04-29 06:59:47+00:00   2.149667  41.309666    0.0\n",
      "1126510  PX773659568615 2020-04-29 07:02:41+00:00   2.149632  41.309608    0.0\n",
      "1126511  PX773659568615 2020-04-29 07:05:44+00:00   2.149630  41.309601    0.0\n",
      "1126512  PX773659568615 2020-04-29 07:11:48+00:00   2.149667  41.309666    0.0\n",
      "\n",
      "[1126513 rows x 5 columns]\n",
      "['SI319357417925' 'TN538330441060' 'QZ477152068239' 'LG911064678403'\n",
      " 'SY803445072525' 'CA965381464224' 'NJ184050490646' 'EF722193487389'\n",
      " 'SM614397332616' 'MI289110566674' 'TT638943897453' 'KY864499841181'\n",
      " 'NO482848046083' 'TZ133602656142' 'HL638230123258' 'SK819484799282'\n",
      " 'UJ633087750675' 'AO393647206772' 'YM817103017080' 'PU558133970444'\n",
      " 'BT719395802096' 'KQ769874181280' 'NZ438363836923' 'GO496250630640'\n",
      " 'OY528340395638' 'ZM490479048723' 'XK281646627950' 'NW995293663764'\n",
      " 'LP672320218364' 'YT360665182781' 'TV642534258644' 'DA972719189591'\n",
      " 'OM565173161930' 'KU436448486469' 'OR346969954620' 'XD359041876792'\n",
      " 'YH418256204193' 'OG507714490566' 'XU747099494874' 'EP128434855744'\n",
      " 'RX185740354780' 'XE918321587279' 'BY440962771393' 'SE842853856950'\n",
      " 'KV831586359603' 'YR153785077825' 'BZ623190611735' 'UE537388907567'\n",
      " 'CV766823251463' 'IG622411972338' 'ZE646628437331' 'LD610349268977'\n",
      " 'PE445854879350' 'VP370083529448' 'GE443124878978' 'DK753961845822'\n",
      " 'FY987917587032' 'PZ633943964452' 'YB225099515850' 'BS141811220707'\n",
      " 'XI501985677597' 'ZB421092044630' 'UW169458290073' 'MR957371720496'\n",
      " 'OE906351735412' 'YS273817247442' 'WB885543325571' 'GR609796977258'\n",
      " 'TN826669794092' 'DE305884854616' 'NR263033611174' 'VC641331250232'\n",
      " 'ZI166705356011' 'CG313455688685' 'PX773659568615' 'TB705465263664'\n",
      " 'EI866215583822' 'EN741330060096' 'IL997509813969' 'OM483312811960'\n",
      " 'YS994853537435' 'TW923621935021']\n",
      "HKHKG-FRFOS (82,)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      " 20%|█▉        | 16/82 [00:00<00:01, 42.86it/s]\u001b[A\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 5.85417e+12\tvalid_0's mse_score: 5.85417e+12\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 5.85417e+12\tvalid_0's mse_score: 5.85417e+12\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 6.18558e+12\tvalid_0's mse_score: 6.18558e+12\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 6.18558e+12\tvalid_0's mse_score: 6.18558e+12\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 6.13657e+12\tvalid_0's mse_score: 6.13657e+12\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 6.13657e+12\tvalid_0's mse_score: 6.13657e+12\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 5.36027e+12\tvalid_0's mse_score: 5.36027e+12\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 5.36027e+12\tvalid_0's mse_score: 5.36027e+12\n",
      "Training until validation scores don't improve for 100 rounds\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 91%|█████████ | 20/22 [1:38:37<05:47, 173.58s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[100]\tvalid_0's l2: 6.24072e+12\tvalid_0's mse_score: 6.24072e+12\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 6.24072e+12\tvalid_0's mse_score: 6.24072e+12\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "  0%|          | 0/142 [00:00<?, ?it/s]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          loadingOrder                 timestamp   longitude   latitude  speed\n",
      "0       LO475942996298 2019-01-10 19:45:23+00:00  114.251770  22.561592    0.0\n",
      "1       BW203892436542 2019-01-10 19:45:23+00:00  114.251770  22.561592    0.0\n",
      "2       RQ133081172395 2019-01-10 19:45:23+00:00  114.251770  22.561592    0.0\n",
      "3       LO475942996298 2019-01-10 20:18:30+00:00  114.251869  22.561466    0.0\n",
      "4       BW203892436542 2019-01-10 20:18:30+00:00  114.251869  22.561466    0.0\n",
      "...                ...                       ...         ...        ...    ...\n",
      "552388  VI327633941369 2020-04-29 06:57:11+00:00  174.824936 -42.647041   25.0\n",
      "552389  VI327633941369 2020-04-29 07:01:52+00:00  174.809067 -42.659939   24.0\n",
      "552390  VI327633941369 2020-04-29 07:05:51+00:00  174.795670 -42.670933   24.0\n",
      "552391  VI327633941369 2020-04-29 07:09:41+00:00  174.783035 -42.681713   24.0\n",
      "552392  VI327633941369 2020-04-29 07:13:21+00:00  174.771118 -42.692150   25.0\n",
      "\n",
      "[552393 rows x 5 columns]\n",
      "['LO475942996298' 'BW203892436542' 'RQ133081172395' 'XN849548630367'\n",
      " 'IJ681517231437' 'KI365405997870' 'DC305057417534' 'JV732268792754'\n",
      " 'HK534872150555' 'WD372715914623' 'PL731071769939' 'VV949724353588'\n",
      " 'ZL413554486586' 'CU769483558707' 'QZ746910984584' 'KB818619030246'\n",
      " 'GO125614762540' 'BH131553715928' 'EM899341060819' 'DZ703504905719'\n",
      " 'WS349358811798' 'DN548323547226' 'PL548251922297' 'GL829801787487'\n",
      " 'VN479873665734' 'RD637279740714' 'NS183456419888' 'SZ596758734137'\n",
      " 'TB205968200526' 'YB109280420693' 'JT837996631189' 'SE983149654716'\n",
      " 'NS939051947671' 'QC149788061255' 'CS781758759442' 'DG863608673388'\n",
      " 'ZL799291940050' 'HV511575140302' 'IG215813849980' 'KD525326203113'\n",
      " 'TU418801214260' 'GD668743447296' 'HW239098811583' 'BI100883948955'\n",
      " 'SM286252493187' 'PW679885312267' 'TN498826954668' 'IO663547963939'\n",
      " 'AE105181004450' 'JK735880332797' 'RT939550770831' 'RD436713394364'\n",
      " 'YV586043893467' 'AE691789827173' 'VF128283478191' 'KY680874360727'\n",
      " 'TM719742883661' 'LD878186030403' 'YP958516682099' 'UI717367417046'\n",
      " 'JM963763784640' 'NZ106555720649' 'IK989882244958' 'JV113146838889'\n",
      " 'RT814865187628' 'OB301716662745' 'MG701064043993' 'XV935359761291'\n",
      " 'FU933409022887' 'DN278210785703' 'TX917168537049' 'WD499348665760'\n",
      " 'YV820954835168' 'DU980498312265' 'TC610789329210' 'LO909029269594'\n",
      " 'VP603286067364' 'UG784747790201' 'XF154894829792' 'JK200471726707'\n",
      " 'KZ266008053554' 'PN343676993656' 'YZ277184052103' 'RS213556076172'\n",
      " 'PP415160216937' 'NE496867536029' 'PS189911069592' 'GC552225780130'\n",
      " 'GW267137281606' 'VX914733808697' 'EH484937034809' 'UN886104493690'\n",
      " 'UZ581597412240' 'IY693467115199' 'CS279647742391' 'TG811881858048'\n",
      " 'ZW759043853748' 'FU315558877449' 'ZS730536922311' 'YU316872216279'\n",
      " 'MB184520118053' 'VH520044440100' 'WZ855784930612' 'VH898314015142'\n",
      " 'TK197653823143' 'KZ117810396315' 'PF372425688591' 'YX783067599734'\n",
      " 'YA234511137850' 'GD520480755023' 'SM553970566120' 'WJ642958836149'\n",
      " 'SV247697029972' 'XB230945949672' 'HE972115821206' 'JW396947329362'\n",
      " 'WC773607290775' 'PF427987248082' 'FG214766732528' 'FH181116917336'\n",
      " 'HO139174112304' 'ZF523155138190' 'YY849356055158' 'DF741594697962'\n",
      " 'RE208662452501' 'KA783123854820' 'GN874151483862' 'XS785055678172'\n",
      " 'LT910973226408' 'II679124810849' 'ND681510834346' 'XZ918185472775'\n",
      " 'HE336110257605' 'JL980103427786' 'GJ593009189032' 'UQ679077679633'\n",
      " 'ZG865045551011' 'BL257376984443' 'MG510678375724' 'EA560452449058'\n",
      " 'YG751543556423' 'VI327633941369']\n",
      "CNYTN-NZAKL (142,)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 1.38954e+10\tvalid_0's mse_score: 1.38954e+10\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 1.6951e+10\tvalid_0's mse_score: 1.6951e+10\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 4.03783e+10\tvalid_0's mse_score: 4.03783e+10\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 4.26413e+10\tvalid_0's mse_score: 4.26413e+10\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 9.54169e+09\tvalid_0's mse_score: 9.54169e+09\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 1.12977e+10\tvalid_0's mse_score: 1.12977e+10\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 3.02684e+10\tvalid_0's mse_score: 3.02684e+10\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 3.54496e+10\tvalid_0's mse_score: 3.54496e+10\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 2.08344e+10\tvalid_0's mse_score: 2.08344e+10\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 2.45886e+10\tvalid_0's mse_score: 2.45886e+10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      " 95%|█████████▌| 21/22 [1:38:51<02:05, 125.92s/it]\n",
      "100%|██████████| 1/1 [00:00<00:00, 200.88it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         loadingOrder                 timestamp   longitude   latitude  speed\n",
      "0      HV805281038652 2019-12-19 15:33:08+00:00  -90.790833  13.927833    0.0\n",
      "1      HV805281038652 2019-12-19 15:57:08+00:00  -90.790833  13.927833    0.0\n",
      "2      HV805281038652 2019-12-19 16:15:09+00:00  -90.790833  13.927833    0.0\n",
      "3      HV805281038652 2019-12-19 16:21:22+00:00  -90.790855  13.927813    0.0\n",
      "4      HV805281038652 2019-12-19 16:22:58+00:00  -90.790001  13.926667    0.0\n",
      "...               ...                       ...         ...        ...    ...\n",
      "20178  HV805281038652 2020-02-13 21:11:59+00:00  114.118111  22.332411    2.0\n",
      "20179  HV805281038652 2020-02-13 21:12:29+00:00  114.118011  22.332563    2.0\n",
      "20180  HV805281038652 2020-02-13 21:13:08+00:00  114.117912  22.332747    1.0\n",
      "20181  HV805281038652 2020-02-13 21:14:08+00:00  114.117737  22.332914    1.0\n",
      "20182  HV805281038652 2020-02-13 21:15:08+00:00  114.117592  22.333038    0.0\n",
      "\n",
      "[20183 rows x 5 columns]\n",
      "['HV805281038652']\n",
      "CNSHA-PAMIT (1,)\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 1.05494e+13\tvalid_0's mse_score: 1.05494e+13\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 1.05494e+13\tvalid_0's mse_score: 1.05494e+13\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 1.05494e+13\tvalid_0's mse_score: 1.05494e+13\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 1.05494e+13\tvalid_0's mse_score: 1.05494e+13\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 1.05494e+13\tvalid_0's mse_score: 1.05494e+13\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 1.05494e+13\tvalid_0's mse_score: 1.05494e+13\n",
      "Training until validation scores don't improve for 100 rounds\n",
      "[100]\tvalid_0's l2: 1.05494e+13\tvalid_0's mse_score: 1.05494e+13\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 1.05494e+13\tvalid_0's mse_score: 1.05494e+13\n",
      "Training until validation scores don't improve for 100 rounds\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 22/22 [1:38:53<00:00, 269.71s/it]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[100]\tvalid_0's l2: 1.05494e+13\tvalid_0's mse_score: 1.05494e+13\n",
      "Early stopping, best iteration is:\n",
      "[1]\tvalid_0's l2: 1.05494e+13\tvalid_0's mse_score: 1.05494e+13\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
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       "      <th>loadingOrder</th>\n",
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       "<p>45456 rows × 9 columns</p>\n",
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      "text/plain": [
       "         loadingOrder                 timestamp   longitude   latitude  \\\n",
       "0      CF946210847851  2019-04-02T02:42:28.000Z  138.471062  40.278787   \n",
       "1      CF946210847851  2019-04-02T02:59:28.000Z  138.552168  40.327785   \n",
       "2      CF946210847851  2019-04-02T03:07:28.000Z  138.588250  40.352542   \n",
       "3      CF946210847851  2019-04-02T03:43:28.000Z  138.751325  40.459447   \n",
       "4      CF946210847851  2019-04-02T04:29:28.000Z  138.969782  40.581485   \n",
       "...               ...                       ...         ...        ...   \n",
       "45451  XG479584941731  2019-01-13T03:56:08.000Z  104.633357   1.630708   \n",
       "45452  XG479584941731  2019-01-13T03:57:08.000Z  104.631958   1.626713   \n",
       "45453  XG479584941731  2019-01-13T03:57:38.000Z  104.631258   1.624615   \n",
       "45454  XG479584941731  2019-01-13T03:58:08.000Z  104.630597   1.622682   \n",
       "45455  XG479584941731  2019-01-13T03:59:08.000Z  104.629178   1.618552   \n",
       "\n",
       "      carrierName   vesselMMSI         onboardDate                   ETA  \\\n",
       "0          OIEQNT  R5480015614 2019-04-02 02:42:28  2019/04/24  19:55:07   \n",
       "1          OIEQNT  R5480015614 2019-04-02 02:42:28  2019/04/24  19:55:07   \n",
       "2          OIEQNT  R5480015614 2019-04-02 02:42:28  2019/04/24  19:55:07   \n",
       "3          OIEQNT  R5480015614 2019-04-02 02:42:28  2019/04/24  19:55:07   \n",
       "4          OIEQNT  R5480015614 2019-04-02 02:42:28  2019/04/24  19:55:07   \n",
       "...           ...          ...                 ...                   ...   \n",
       "45451      JCMFTA  U2218600548 2019-01-10 00:27:58  2019/01/30  20:01:57   \n",
       "45452      JCMFTA  U2218600548 2019-01-10 00:27:58  2019/01/30  20:01:57   \n",
       "45453      JCMFTA  U2218600548 2019-01-10 00:27:58  2019/01/30  20:01:57   \n",
       "45454      JCMFTA  U2218600548 2019-01-10 00:27:58  2019/01/30  20:01:57   \n",
       "45455      JCMFTA  U2218600548 2019-01-10 00:27:58  2019/01/30  20:01:57   \n",
       "\n",
       "                  creatDate  \n",
       "0      2020/06/23  20:57:21  \n",
       "1      2020/06/23  20:57:21  \n",
       "2      2020/06/23  20:57:21  \n",
       "3      2020/06/23  20:57:21  \n",
       "4      2020/06/23  20:57:21  \n",
       "...                     ...  \n",
       "45451  2020/06/23  20:57:21  \n",
       "45452  2020/06/23  20:57:21  \n",
       "45453  2020/06/23  20:57:21  \n",
       "45454  2020/06/23  20:57:21  \n",
       "45455  2020/06/23  20:57:21  \n",
       "\n",
       "[45456 rows x 9 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for route in tqdm(test_order_belong_to_trace):\n",
    "    route_order_info = get_train_route_order_data(route)\n",
    "\n",
    "    parameter_mean, parameter_std, train_data = get_train_data(route_order_info, route)\n",
    "\n",
    "    features = [c for c in train_data.columns if c not in ['loadingOrder', 'label']]\n",
    "    model_by_route = train_model(train_data[features], train_data['label'])\n",
    "    \n",
    "    for order in test_order_belong_to_trace[route]:\n",
    "        test_order_data = get_test_data(order,parameter_mean, parameter_std)\n",
    "        res = model_by_route.predict(test_order_data[features], num_iteration=model_by_route.best_iteration)\n",
    "        test_data_origin.loc[test_data_origin['loadingOrder'] == order, 'ETA'] = (test_data_origin[test_data_origin['loadingOrder'] == order]['onboardDate'] + pd.Timedelta(seconds=res[0])).apply(lambda x:x.strftime('%Y/%m/%d  %H:%M:%S'))\n",
    "    \n",
    "test_data_origin['creatDate'] = pd.datetime.now().strftime('%Y/%m/%d  %H:%M:%S')\n",
    "test_data_origin['timestamp'] = test_data_origin['temp_timestamp']\n",
    "\n",
    "result = test_data_origin[['loadingOrder', 'timestamp', 'longitude', 'latitude', 'carrierName', 'vesselMMSI', 'onboardDate', 'ETA', 'creatDate']]\n",
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "result.to_csv(result_path, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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